## ---- include = FALSE--------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ---- eval=FALSE-------------------------------------------------------------- # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # # BiocManager::install("GSEAmining") ## ---- eval=FALSE-------------------------------------------------------------- # install.packages("devtools") # If you have not installed "devtools" package # library(devtools) # devtools::install_github("oriolarques/GSEAmining") ## ---- eval=FALSE-------------------------------------------------------------- # # A geneList contains three features: # # 1.numeric vector: fold change or other type of numerical variable # # 2.named vector: every number has a name, the corresponding gene ID # # 3.sorted vector: number should be sorted in decreasing order # tableTop_p30 <- as.data.frame(tableTop_p30) # geneList = tableTop_p30[,2] # names(geneList) = as.character(tableTop_p30[,1]) ## ---- eval=FALSE-------------------------------------------------------------- # library(clusterProfiler) # # Read the .gmt file from MSigDB # gmtC2<- read.gmt("gmt files/c2.all.v7.1.symbols.gmt") # gmtC5<- read.gmt('gmt files/c5.all.v7.1.symbols.gmt') # gmtHALL <- read.gmt('gmt files/h.all.v7.1.symbols.gmt') # # # Merge all the gene sets # gmt_all <- rbind(gmtC2, gmtC5, gmtHALL) ## ---- eval=FALSE-------------------------------------------------------------- # GSEA_p30<-GSEA(geneList, TERM2GENE = gmt_all, nPerm = 1000, pvalueCutoff = 0.5) # # # Selection of gene sets with a specific thershold in terms of NES and p.adjust # genesets_sel <- GSEA_p30@result ## ----------------------------------------------------------------------------- # Structure of the data included in the package data('genesets_sel', package = 'GSEAmining') tibble::glimpse(genesets_sel) ## ----------------------------------------------------------------------------- library(GSEAmining) data("genesets_sel", package = 'GSEAmining') gs.filt <- gm_filter(genesets_sel, p.adj = 0.05, neg_NES = 2.6, pos_NES = 2) ## ----setup-------------------------------------------------------------------- # Create an object that will contain the cluster of gene sets. gs.cl <- gm_clust(gs.filt) ## ---- fig.height = 7, fig.width = 7------------------------------------------- gm_dendplot(gs.filt, gs.cl) ## ---- fig.height = 7, fig.width = 7------------------------------------------- gm_dendplot(gs.filt, gs.cl, col_pos = 'orange', col_neg = 'black', rect = TRUE, dend_len = 20, rect_len = 2) ## ---- message = FALSE, fig.height = 7, fig.width = 7-------------------------- gm_enrichterms(gs.filt, gs.cl) ## ---- message = FALSE, fig.height = 7, fig.width = 7-------------------------- gm_enrichterms(gs.filt, gs.cl, clust = FALSE, col_pos = 'chocolate3', col_neg = 'skyblue3') ## ---- message = FALSE, fig.height = 12, fig.width = 7.2----------------------- gm_enrichcores(gs.filt, gs.cl) ## ---- eval=FALSE-------------------------------------------------------------- # gm_enrichreport(gs.filt, gs.cl, output = 'gm_report') ## ----------------------------------------------------------------------------- sessionInfo()